Outlier Detection In Financial Statements: A Text Mining Method
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S. S. Kamaruddin, A. R. Hamdan, A. Abu Bakar & F. Mat Nor
This paper presents a text mining methodology to extract outlying knowledge from a collection of financial statements. The main idea is to extract relevant financial performance indicators and discover implicit textual description of the indicators. The extracted information was represented using a network language i.e. conceptual graph. Outlier mining was performed on the conceptual graph representation using a deviation based method. Experiments were carried out to evaluate the effectiveness of the proposed method. Results show that the proposed method is able to excerpt outlying knowledge from the financial statements with accuracy comparable to human experts. Keywords: text mining, information extraction, conceptual graphs, outlier mining in text, deviation based outlier mining method.
text mining, information extraction, conceptual graphs, outlier mining in text, deviation based outlier mining method.